Smart factory under Industry 4.0 and industrial Internet of Thighs (IoT) has attracted much attention from both academia and industry. In wireless industrial networks, massive Machine Type Communication (mMTC) can effectively support a variety of IoT applications, where industrial IoT (IIoT) and IoT devices have different quality-of-service (QoS) requirements, ranging from high reliability and low-latency to high transmission data rates. These industrial networks will be highly complex and heterogeneous, as well as the spectrum and energy resources are severely limited. Hence, this paper presents a heterogeneous radio frequency (RF)/visible light communication (VLC) industrial network architecture to guarantee the different QoS requirements, where RF is capable of offering wide-area coverage and VLC has the ability to provide high transmission data rate. A joint uplink and downlink energy-efficient resource management decision-making problem (network selection, subchannel assignment and power management) is formulated as a Markov decision process. In addition, a new deep post-decision state (PDS) based experience replay and transfer (PDS-ERT) reinforcement learning algorithm is proposed to learn the optimal policy. Simulation results corroborate the superiority in performance of the presented heterogeneous network, and verify that the proposed PDS-ERT learning algorithm outperforms other existing algorithms in terms of meeting the energy efficiency and the QoS requirements.

In this paper, a low-complexity time-difference-of-arrival (TDOA)-based indoor visible light positioning (VLP) system using an enhanced practical localization scheme based on cross-correlation is proposed and experimentally demonstrated. The proposed TDOA scheme offers two advantages: 1) the use of virtual local oscillator (VLO) to replace the real local oscillator (RLO) for cross-correlation at the receiver side so as to reduce the hardware complexity; 2) the application of cubic spline interpolation on the correlation function to reduce the rigorous requirement on the sampling rate and to enhance the time-resolution of cross-correlation. In order to achieve high positioning accuracy with minimum implementation complexity, parameter optimization is first performed in terms of sampling rate, interpolation factor and data length for correlation. Using the obtained optimal parameters, we demonstrate a low-complexity indoor two-dimensional (2D) VLP system using the correlation-based TDOA scheme in a coverage area of 1.2$\times$1.2 m$^{2}$ with a height of 2 m. The experimental results validate the feasibility of the proposed TDOA scheme, and an average positioning accuracy of 9.2 cm is achieved with a sampling rate of 500 MSa/s, an interpolation factor of 100 and a data length of 250k samples.

A non-contact, non-invasive monitoring system to measure and estimate the heart and breathing rate of humans using a frequency-modulated continuous wave (FMCW) mm-wave radar at 77 GHz is presented. A novel diagnostic system is proposed which extracts heartbeat phase signals from the FMCW radar (reconstructed using Fourier series analysis) to test a three-layer artificial neural network model to predict the presence of arrhythmia in individuals. The effect of person orientation, distance of measurement and movement was analyzed with respect to a reference device based on statistical measures that include number of outliers, mean, mean squared error (MSE), mean absolute error (MAE), median absolute error (medAE), skewness, standard deviation (SD) and R-squared values. The individual oriented in front of the radar outperformed almost all other orientations for most distances with an expected d = 90 cm and d = 120 cm. Furthermore, it was found that the heart rate that was measured while walking and the breathing rate which was measured for a motionless individual generated results with the lowest SD and MSE. An artificial neural network (ANN) was trained using the MIT-BIH database with a training accuracy of 93.9 % and an R² value = 0.876. The diagnostic tool was tested on 15 subjects and achieved a mean test accuracy of 75%.

Internet of things (IoT) has attracted much interest due to its wide applications such as smart city, manufacturing, transportation, and healthcare. Social and cognitive IoT is capable of exploiting the social networking characteristics to optimize the network performance. Considering the fact that IoT devices have different quality of service (QoS) requirements (ranging from ultra-reliable and low-latency communications (URLLC) to minimum data rate), this paper presents a QoS-driven social-aware enhanced device-to-device (D2D) communication network model for social and cognitive IoT by utilizing social orientation information. We model the optimization problem as a multi-agent reinforcement learning formulation, and a novel coordinated multi-agent deep reinforcement learning based resource management approach is proposed to optimize the joint radio block assignment and transmission power control strategy. Meanwhile, prioritized experience replay (PER) and coordinated learning mechanisms are employed to enable communication links to work cooperatively in a distributed manner, which enhances the network performance and access success probability. Simulation results corroborate the superiority in the performance of the presented resource management approach, and it outperforms other existing approaches in terms of meeting the energy efficiency and the QoS requirements.

With the rapid development of Internet of Things (IoT) in the smart city, smart grid and smart industry, indoor communication and positioning are important fields of applications for indoor IoT. This paper presents an integrated visible light communication and positioning (VLCP) system for indoor IoT, in order to provide the high-speed data rate and high-accuracy positioning for IoT devices. where the filter bank multicarrier-based subcarrier multiplexing (FBMCSCM) technique is exploited to effectively reduce the out-ofband interference (OOBI) on both adjacent communication and positioning subcarriers. After that, we propose a coordinated resource allocation approach for the system with the purpose of maximizing the sum rate while guaranteeing the minimum data rates and positioning accuracy requirements of devices. To this end, we solve the optimization problem by decomposing it into two subproblems, where a low-complexity suboptimal subcarrier allocation approach is proposed and the sequential quadratic programming (SQP) method is adopted to solve the non-linearly constrained power allocation optimization problem. Numerical results verify the superiority in performance of the presented integrated VLCP system for indoor IoT, and the results also reveal that the proposed coordinated resource allocation approach can effectively improve the sum rate and the positioning accuracy compared with other resource allocation approaches.